Protect the meaningful contributors
Prioritize Outerwear & Coats, Jeans, and Sweaters. Review Jumpsuits & Rompers, Active, and Leggings for a lower allocation, with inventory and margin checks before any cuts.
The assortment
Slowest-growing
doesn't mean shrinking.
Every category grew. Even Jumpsuits & Rompers, the slowest, increased revenue by 80.79% and gross profit by 79.11%.
The question is relative priority, not whether these products have stopped selling. Active and Leggings also sit near the bottom of both rankings.
One assortment. Two growth measures.
2022 versus 2021The contribution
A small growth rate
can carry a lot of value.
Outerwear & Coats, Jeans, and Sweaters account for 31.54% of the increase in gross profit. They deserve attention even though none is the fastest-growing category.
Jumpsuits & Rompers, Active, and Leggings contribute much less and grow more slowly. They are candidates for lower priority, not automatic removal.
Growth meets contribution
Why this isn't a literal BCG matrix
The portfolio framework is BCG-inspired, but no competitor or industry market-share data is available. The source sheet calculates each category's increase in gross profit divided by the total increase. This chart uses that calculation and names it directly.
The shortlisted investment categories are above 100% growth. They are substantial contributors, not established low-growth cash generators. The framework supports a resource-allocation discussion; it does not prove the return on additional spending.
The first purchase
More people
made their first order.
13,822 buyers made their first completed order in 2022. The monthly cohort grew from 779 in January to 1,895 in December: an increase of 143.26%.
That is growth in first-time completed buyers, not evidence that existing buyers ordered more often. The next question is what happened after that first purchase.
A larger starting audience
New completed-order buyersThe return
A first purchase
is only the beginning.
Only 25 of January's 779 buyers recorded an order in the following month: 3.21%. November's equivalent rate was 12.76%, or 200 of 1,568 buyers.
Later cohorts show stronger next-month activity. But they have less follow-up time, so compare them at the same month after entry.
A definition that matters
Entry requires a completed order. Later activity includes any order status, because the activity query has no completion filter. These rates measure recorded order activity, not confirmed repeat-purchase retention.
Who came back, and when?
Months after first completed order
Lower activityHigher activityNot yet observed
Compare at the same point in time
7.48%
had next-month order activity across the January–November cohorts.892 of 11,927 eligible buyers. December is excluded because its next month is outside the observation window.
The next move
Invest with focus.
Make the return worth testing.
Test the second-order incentive
Trial a first-buyer coupon against a control group. Measure completed second orders and incremental gross profit after discounts, not just recorded activity.
Keep existing buyers in the plan
Test relevant offers for returning customers. Use the later cohorts' stronger early activity as a hypothesis to investigate, not proof that a promotion caused improvement.
A bigger audience creates opportunity.
A valuable return makes it sustainable.
Behind the analysis
The logic is
part of the finding.
Products + order itemsJoin → completed items → annual category totals
Orders + first-order cohortsFirst completed order → distinct user-month activity
Scope, definitions & limits
Growth compares completed order items created in 2021 and 2022, joined to product category and product cost. Gross profit excludes operating expenses, acquisition costs and other costs beyond the product-cost field. It is not net profit.
Cohorts use the first completed order across all available history. Later activity is deduplicated by user and month, restricted to calendar 2022, but not restricted by order status. A cohort member can reappear after missing a month. The data does not establish why cohort sizes or activity rates changed.
The article preserves the historical results saved in the analysis sheets. Category-growth labels and all 78 cohort cells were checked against those sheets; derived totals and contribution shares were recalculated. The historical BigQuery tables were not rerun. The displayed SQL is a clearer, consolidated form of the documented logic. The completed-only version is a proposed follow-up, not the source of the displayed rates.
Category-contribution reference lines are descriptive choices, not statistical tests. External competitor share, acquisition costs, campaign exposure and incremental campaign outcomes are not supplied. No measured coupon lift or investment return is claimed.
Sources & working data
01 Revenue-growth results 02 Gross profit & contribution calculations 03 Cohort query resultsDataset context: Google Cloud on the fictional TheLook dataset. Framework context: BCG's growth-share matrix.